Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (2): 133-141.

doi: 10.11871/jfdc.issn.2096-742X.2021.02.015

• Technology and Applicaton • Previous Articles    

A Deep Learning Based Method for Remote Sensing Image Parcel Segmentation

ZHAO Weiyu1,2(),ZHANG Honghai1,*(),ZHONG Bo3()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China
  • Received:2020-12-31 Online:2021-04-20 Published:2021-05-18
  • Contact: ZHANG Honghai


[Objective] Remote sensing image parcel segmentation is a specific task of remote sensing image interpretation. Good results of remote sensing image parcel segmentation can provide guidance for environmental protection, agricultural production and town construction. [Methods] In this paper, DeepLabV3+ network is built using Pytorch framework. The encoder uses ResNet101 and atrous spatial pyramid pooling module for feature extraction, and the decoder uses the bilinear interpolation method for feature map resizing. During the training process, a data augmentation strategy is specifically designed for the remote sensing image parcel segmentation task, so as to enhance the generalization ability of the model. A loss function with joint Lovasz loss and Softmax loss is used to solve the problem of unbalanced distribution of sample categories. [Results] The mean Intersection over Union is chosen as the evaluation index for the experimental results. The mean Intersection over Union of the final model can reach 70.3%, which is 7.6% higher than the UNet, a commonly used method for remote sensing image segmentation. [Limitations] The region of some segmented images is not complete, and the connectivity of segmented images needs to be further improved. [Conclusions] The remote sensing image parcel segmentation method proposed in this paper can achieve fine segmentation results of high-resolution remote sensing images and provide a reference for the research of remote sensing image segmentation.

Key words: remote sensing image, image segmentation, deep learning